Abstract

No rational investor wants to make a large payment for a purchase whose quantity, quality, and longevity are largely unknown. However, that is essentially what is required of most modern petroleum firms that seek to explore and develop new prospective areas.

To obtain contractual rights to explore for and develop petroleum resources in most countries, a cor-poration usually must commit to spending millions of dollars, either through work commitments (line-miles of seismic surveys, number of drilled wells, and the like) or front-end payments (bonus bids, fees, and the like), or both. Frequently, such financial commitments are undertaken with only minimal knowledge about the prospectivity of the contract area—how many new fields may be discovered; how much oil and/or gas they may contain; how much it may cost to find, develop, and produce them; how profitable they may be; how long it may take to establish production; and how long the productive life of the fields may be.

Ideally, such exploration would be staged: progressive investments would be closely related to the ongoing acquisition of geotechnical, economic, and political information bearing on evolving perceptions of risk versus reward, thus minimizing unnecessary expenditures. However, the form of most existing international contracts prevents such prudent investing.

Thus the most critical decision in modern petroleum exploration is not which prospect to drill. Rather, it is which new trend or area to go into, because that decision commits the organization to millions of invested dollars, years of involvement, and hundreds of man-years of professional and technical effort.

Introduction

The Problem with Most Exploration Contracts

No rational investor wants to make a large payment for a purchase whose quantity, quality, and longevity are largely unknown. However, that is essentially what is required of most modern petroleum firms that seek to explore and develop new prospective areas.

To obtain contractual rights to explore for and develop petroleum resources in most countries, a cor-poration usually must commit to spending millions of dollars, either through work commitments (line-miles of seismic surveys, number of drilled wells, and the like) or front-end payments (bonus bids, fees, and the like), or both. Frequently, such financial commitments are undertaken with only minimal knowledge about the prospectivity of the contract area—how many new fields may be discovered; how much oil and/or gas they may contain; how much it may cost to find, develop, and produce them; how profitable they may be; how long it may take to establish production; and how long the productive life of the fields may be.

Ideally, such exploration would be staged: progressive investments would be closely related to the ongoing acquisition of geotechnical, economic, and political information bearing on evolving perceptions of risk versus reward, thus minimizing unnecessary expenditures. However, the form of most existing international contracts prevents such prudent investing.

Thus the most critical decision in modern petroleum exploration is not which prospect to drill. Rather, it is which new trend or area to go into, because that decision commits the organization to millions of invested dollars, years of involvement, and hundreds of man-years of professional and technical effort. If the selected new area proves to contain economic petroleum reserves, then substantial value has been acquired by the firm. Yet such commitments commonly have been made without disciplined, integrated geotechnical and economic evaluation. Almost every large oil company can relate disastrous experiences with international contract areas, in which dramatic economic losses were experienced over multiyear contract periods, com-monly stemming from undisciplined, even haphazard entry decisions into new trends or basins.

Fortunately, the problem is tractable. Explorationists recognize that oil and gas fields occur in “families”— groups of fields of common geologic origin, usually in geologically definable areas or trends. These groups of fields have similar producing attributes and similar economic patterns. We call such related groups of fields and prospects “plays” (p. 3). And plays can be evaluated as full-cycle economic ventures.

History and Development of the Play Concept

The era of modern petroleum exploration began in 1859 in Pennsylvania (Owen, 1975; Pees, 1989; Yergin, 1991). As drilling for oil spread in the United States and in eastern Europe during the late 19th century, and then more widely around the world during the first half of the 20th century, petroleum exploration focused on the prospect: “A documented set of anomalous geological criteria that, in combination with related economic circumstances, justifies the capital investment of drilling an exploratory well to discover a hypothecated commercial accumulation of oil and/or natural gas” (modified after Levorson, 1967).

By 1935 geologists recognized several different types of petroleum accumulations—anticlinal traps, fault traps, stratigraphic traps, combination traps, hydrodynamic traps, and so on. Moreover, experienced geologists knew that certain types of petroleum accumulations were characteristic in some basins or trends but rare or even unknown in others.

Beginning in the 1950s, petroleum exploration researchers began to focus on stratigraphy and depositional systems, trying to understand and predict the origin and distribution of sedimentary facies as they relate to the occurrence of reservoirs, seals, and hydrocarbon source beds. That research campaign, directed in substantial part at modern depositional processes, constituted a revolution in sedimentary geology, especially with regard to industry knowledge about different types of reservoir bodies.

In the late 1960s another new subdiscipline—sedi-mentary basin analysis (Miall, 1984)—arose in petroleum exploration, abetted in part by the emerging revelations of plate tectonics (Dickinson, 1974; Klemme, 1975, 1980; Bally and Snelson, 1980) and in part by computer-processed CDP seismic data, which could now sense and depict the subsurface configuration and internal geometry of specific sedimentary sequences (Vail et al., 1977a, 1977b). Now geologists could relate the distribution of reservoir and sealing rock bodies to geologic structure, in terms of (1) origin, provenance, and regional setting, as well as (2) subsequent structural evolution and present configuration.

These geotechnical concepts and tools all contributed to the development of the exploration play concept, first used by the Geological Survey of Canada in 1972 (McCrossan, 1973; Roy, 1975; Miller, 1986) and later well described by Baker et al. (1986): a group of prospects as well as oil and/or gas fields, all having similar geologic origins—a family of geologically similar traps. Fields comprising a play contain similar reservoir rocks that arose from similar depositional processes, and the constituent field reservoirs exhibit similar production patterns. Prospects and fields in a play have similar structural configurations and structural histories. They have similar top seals and seat seals. Also, fields in a play form a coherent lognormal distribution of ultimately recoverable reserves. Other names for the same thing include genetic trend, geologic fairway, petroleum zone, complex of fields, and producing trend.

Exxon geologists published important papers on various aspects of play analysis during the 1975-1995 period, especially David White (1980, 1988, 1992, 1993) and R.A. Baker (1986, 1988). Sluijk and Nederlof (1984) described Shell's complex system for prospect and play analysis. Serial publications by the Texas Bureau of Economic Geology staff during 1985-1995 described and summarized many plays in Texas. Otis and Schneider-mann (1997) published an excellent and detailed summary of Chevron's exploration risking procedure.

Perceptive prospectors recognized that when companies carried out exploration campaigns along structural or stratigraphic fairways, they were usually engaged in play exploration. Geophysicists could lay out regional seismic grids and stratigraphers could deal with depositional trends. This was a much more efficient way to conduct exploration. Once the key geologic and geotechnical patterns and relationships became understood—once a company had “cracked the exploration code” and discovered the first field— discovery of other fields in the play was greatly facilitated. Thus, play exploration was a form of geo-technical leveraging, and it often led to multiple dis-coveries and highly profitable “core areas.” Moreover, field development became more efficient because the fields in a play usually had similar reservoir conditions, thus similar development and production techniques could be applied to the related fields in the play. Learning facilitated profitability.

But exploration economics tended to focus on the single prospect as the economic unit of exploration, even though companies didn't ordinarily explore on the basis of isolated, unrelated prospects. Instead they directed their attention to the play as the operational unit of exploration. And the unifying attribute of the play was the overall similarity of traps—reservoir rocks, structural and stratigraphic configurations, and sealing facies. The U.S. Geological Survey and U.S. Minerals Management Services employed the play concept in carrying out several assessments of remaining onshore and offshore U.S. oil and gas resources during the 1990s. Their databases are now available to the public, at no charge, on CD-ROM (1995, 1997, 2000).

Plays and Petroleum Systems

In the early 1970s ongoing research in petroleum geochemistry began to bear fruit, leading to the widespread recognition that oil and natural gas could be classified into distinct geochemical types, and that they were generated from kerogen-rich, generally finegrained sediments in petroleum-generative depressions (Demaison, 1984) through processes that were time- and temperature-dependent (Waples, 1980). Later geochemical work recognized that kinetics played a dominant role in the generation process (Hunt et al., 1991; Waples, 1994). Nevertheless, integrated geologic methods of analysis and mapping could identify such thermally mature basins, or “kitchens,” as well as the probable timing and directions of subsequent migration of generated petroleum.

With these geochemical capabilities, explorationists could now demonstrate what had long been sus-pected—that fields in a play also had similar histories of hydrocarbon type, origin, emplacement, and preservation. Armed with these powerful geochemical con-cepts and analytic tools, explorationists began to exploit the play concept worldwide.

By about 1975, regional explorationists began to develop yet another unifying concept, employing the concepts and techniques described previously: the concept of the petroleum system (Magoon and Dow, 1994). Here the focus was on the entire petroleum-generative basin complex of petroleum source rocks, carrier beds and conduits for migrating petroleum, and traps containing the reservoired hydrocarbons. A petroleum system often contains multiple plays with several different types of traps, all charged from a common petroleum source rock in a petroleum-generative depression, or kitchen (Figures 26 and 27) (Demaison, 1984). Magoon's emphasis on the “critical moment” (Figure 28) facilitates critical thinking and investigations bearing on time of peak hydrocarbon generation and probable migration routes to traps available at that time. Accordingly, the petroleum system approach provides a very powerful framework for understanding the basin's “plumbing system,” and it stimulates and encourages the development of new exploration ideas and targets (Table 11). However, it does not lend itself to economic analysis because of the complexities attendant upon the multiple and dissimilar constituent plays (Figure 29).

Map of the hypothetical basin showing areal extent of the petroleum system. From Magoon and Dow (1994).

Possibly because of their geoscience research focus, leading petroleum system authorities define the geologic play differently than do most practical petroleum explorationists. Magoon and Dow (1994), for example, conceive of the petroleum system as including only those elements that are known—proven source rocks, identified carrier beds and conduits, and discovered fields whose analyzed oils and natural gases are clearly related to the source rocks. Following that definition, a play is only that part of the petroleum system that is undiscovered (Magoon, personal communication, 1997).

To the practical explorationist, such a definition causes substantial problems: First, it allows several different families of traps, if charged from a common mature source rock, to be included within a single play. This presents extremely complex consequences for companies that wish to analyze an entire play as a full-cycle business venture because of very wide variances caused by the diversity of trap types, geologic dependencies, and profitabilities. Second, it makes use of field-size distributions (FSDs)—a very powerful predictive tool in economic play analysis—much less discriminating because different genetic trap types must be included in the same distribution. This is a serious statistical drawback. Third, it artificially separates prospects and leads from discovered producing fields in the play and thus hinders our use of field analogs and models in exploration. Finally, the preferred definition of the play, a family of geologically related fields, prospects and leads, all of similar geological origin and charged from common petroleum source beds, does not in any way hinder the employment of the petroleum system as an extremely useful exploration concept. The writer strongly urges active explorers to follow the above-recommended definition of the exploration play.

Events chart showing relationship between essential elements and processes as well as preservation time and critical moment. From Magoon and Dow (1994).

Play Selection as the Critical Exploration Decision

With the previous considerations in mind, and recognizing the prevalence in international exploration of large contract areas obtained through various combinations of costly work commitments and/or front-end bonuses, it is clear that the most important decision in international exploration is not which prospect to drill. Instead, the key decision concerns which new play to enter because it involves much larger commitments of money, time, and personnel. The economic consequences of choosing a bad play can be serious to a medium-sized international explorer; to a smaller firm such an outcome can be financially disastrous.

Table 11

Factor comparison in the four levels of petroleum investigation (Magoon & Dow, 1994).

The good news is that, because of (1) similar reservoir types and trap geometries, (2) commonality of hydrocarbon charge, (3) consistent exploration and development methods, and (4) uniform contract terms, it is possible to carry out economic evaluations of exploration plays as full-cycle economic ventures. Such evaluations utilize regional geologic, geochemi-cal, and geophysical studies (see Appendix E), and they are carried out using basic principles of prospect risk analysis, as described in Chapter 3.

Risk Analysis of Prospects and Plays Compared

For the most part, the logic and procedures used in conducting risk analysis and economic assessment of an exploration play are identical to, derived from, or analogous with those employed in prospect risk analysis (Table 12).

For example, just as a series of successful development wells prove up the discovered field, so do successive discoveries prove up the successful play (Baker et al., 1986). The prospect-reserves distribution allows probabilistic prediction of reserves expected, given that the prospect proves to be a discovery; similarly, the FSD allows probabilistic reserves prediction for the new play, given at least one discovery. Either distribution can be used to help estimate the chance of commercial success (see p. 39) and/or the chance of economic success, given the presence of flowable hydrocarbons. For distributions of both prospect reserves and field sizes, it is important to determine minimum commercial field size (MCFS) or minimum economic field size (MEFS) and the probabilities associated with them (Pmcfs and Pmefs). No prospect should be drilled unless the operator has a legitimate reason to expect the venture to be profitable on a full-cycle economic basis (i.e., the mean-reserves case is at least minimally economic). However, the operative decision to complete the discovery well, made after the well has been drilled, is based on the MCFS simply because the decision to complete the discovery well considers all prior costs as “sunk” and thus depends only on whether costs of completion and operation allow an acceptable profit-making investment on a point-forward basis. For plays, however, MEFS is preferred because no rational company is purposefully going to continue to explore for and develop new fields in a play where the discovered fields are not economic on a full-cycle basis. For both prospects and plays, we must construct detailed cash-flow models based on parameters estimated through objective and reliable geotechnology and contract analysis to assess project profitability on a full-cycle economic basis, first for the anticipated new fields (prospects) and then for the anticipated new core producing area (the play).

However, there are also some important differences between prospect and play risk analysis. For single-objective wildcats, the geologic chance factors used to estimate the chance of encountering flowable hydrocarbons (= geologic success, Pg) are treated as if they were independent of each other. This assumption allows serial multiplication of the individual components of geologic chance to yield Pg. However, for exploratory wells having multiple objectives, risk analysis recognizes that some chance factors are com-mon to all the objective zones, whereas other chance elements vary among the different objective zones (p. 41 and Appendix D). The chance factors that are shared (that is, common to all the objective zones) frequently include petroleum generation and migration, including the elements of timing and preservation. The independent chance factors typically include reservoir, closure, and containment. To correctly estimate the chance of various combinations of discoveries for such multiple-objective prospects, it is necessary to calculate the chance of success of the shared factors separately from the independent chance factors.

This same principle is also consistently employed in risk analysis of plays, simply because there are, characteristically, elements of geologic chance that apply equally to every prospect in the play (= “shared chance”); similarly there are also elements of geologic chance that vary among the different prospects (= “average prospect chance” or “local chance”). To make things even more complicated, we may also deal with partial dependencies, where a given geologic chance factor is partly shared and partly independent.

In carrying out risk analysis of plays, especially new plays, the elements of geologic chance associated with hydrocarbon generation, migration, and timing are usually of paramount importance to address and satisfy. However, for plays having greater exploration maturity or plays in known petroliferous basins, concerns about hydrocarbon charge commonly are diminished, whereas concerns about reservoir, closure, and containment are greater. Nevertheless, all chance factors are theoretically of equal importance in the sense that all must have been satisfied in order for a reser-voired accumulation to exist. As pointed out previously (p. 34), the chance factors should be thought of as if they were equal links in a circular chain: if one link is broken, the chain is broken.

Another significant difference between plays and prospects concerns the strategies involved after discovery: generally, it is important to develop the newly successful prospect promptly and begin generating production revenues as soon as possible because of the time value of money. But most successful plays involve several sequentially discovered and developed fields. Moreover, early production revenues may themselves generate funds required for subsequent development of fields within the play or core area. Construction of pipelines and/or development of markets for production may take several years. Accordingly, in some plays there may be legitimate reasons for staging the exploration and development, consistent with maximum long-term profitability. Often such delays may constitute pragmatic, nonquantitative applications of option-pricing theory (see p. 52).

Integration of Geotechnology, Economics, and Management in Play Analysis

There are many ways to convert a viable play into either an economic loss or an unappreciated, rejected investment opportunity. Whether the error has to do with bad geotechnology, bad engineering, bad economic evaluation, or bad acquisition strategies, the consequences are the same. That's why successful risk analysis of exploration plays requires thorough synthesis of multidisciplinary topics: geostatistics, geology, geophysics, reservoir engineering, drilling and completion technology, economics, decision analysis, contract analysis, political analysis, industrial operations, and business strategies. These specialties take on greater or lesser importance at different stages in the life of the successful play, but their successful integration is required if the play is to succeed as a full-cycle economic risk venture.

Accordingly, the most common organizational pattern adopted by the modern petroleum industry for analyzing and exploring new plays is the multidisci-plinary exploration team. The composition of such teams may change in both numbers and specialties as the play evolves.

Important Geologic Concepts for Play Analysis

Appendix E outlines topics, procedures, and key steps in carrying out a geologic synthesis of an explo-ration play. Such a synthesis—“total geology”— integrates all geologic elements bearing on the likelihood of petroleum accumulations, their possible volumes, spatial distribution, and geologic character. Such integrated studies form the basis for any subsequent legitimate attempt at risk analysis and economic assessment of a new exploration play, or entry into an ongoing one.

It is far beyond the scope of this book to delve deeply into the subject of geologic basin analysis or even to summarize such a complex topic. Appendix E is, however, a useful flow sheet to guide explorationists through the process, so that they will end up with the key maps, cross sections, and data necessary for the estimates and calculations required in the sequential steps of formal play analysis. In addition, several geologic concepts are especially important to consider in conducting a sound play analysis.

Depositional topography interpretation indicates that all of the shelf limestone is older than any of the marine shale. The only facies relationship is between the shelf limestone and marine limestone, with basin starvation inferred.

Depositional topography interpretation indicates that all of the shelf limestone is older than any of the marine shale. The only facies relationship is between the shelf limestone and marine limestone, with basin starvation inferred.

Stratigraphic Sequences in Play Analysis

The topic of sequence stratigraphy has been a rapidly expanding field of geologic study since the mid-1970s. Utilizing the principles of sequence stratigraphy is extremely important in carrying out geotech-nical reviews and syntheses of new basins and plays.

L.L. Sloss (1963) published the first comprehensive treatment of continental-scale stratigraphic sequences, identifying six unconformity-bounded “packages” of sedimentary rocks on the North American continent. Sloss's original sequences each represented about 50 to 120 million years of geologic history. Later work by Sloss's student, Peter Vail, and his Exxon associates led to the recognition of a hierarchy of ordered sequences— cycles spanning diminishing intervals of geologic time (Vail et al., 1977a, 1977b). For petroleum geoscientists concerned with efficient evaluation of basins and plays, sequence stratigraphy is important for three reasons.

Regional Mapping

One of the first tasks facing the geoscientist who is entering a new basin or play is to identify natural cycles—unconformity-bounded stratigraphic units— and to make maps and cross sections using such natural sedimentary packages and especially the unconformable surfaces bounding them. The true spatial and geologic relationships between source rocks, reservoir rocks, and sealing rocks are best understood in context with these natural chapters of earth history. In particular, sedimentation related to contemporaneous structural events and sedimentary provenance is most readily deciphered at a regional scale when geoscientists think in terms of stratigraphic sequences and subsequences.

Depositional Topography

Traditional stratigraphy has not dealt comfortably with depositional topography in sedimentary sequences. Figure 30a shows how the characteristic changes in rock type and thickness are conventionally interpreted as facies changes of coeval sedimentary deposits, whereas Figure 30b deals more realistically with the same rock patterns, as a combination of different facies and depositional topography. Depositional topography is significant and important to recognize in play scale geologic analysis because it allows the geoscientist to understand the true spatial relationships of various constituents of the petroleum system—source rocks, carrier beds, reservoir rock, and seals. These are most readily detected where the cross section or mapping datum is a regional unconformity, either at the base or the top of the subject sequence or subsequence.

Predictions Based on Sea-level Stands

Modern sequence stratigraphers understand that sea level has risen and fallen, often dramatically, in the geologic past, and—in context with depositional topography—has produced characteristic depositional models and lithologic patterns. Stratigraphers may look for and predict certain combinations of petroleum facies associated with highstand, transgressive, and lowstand deposits. In particular, just the awareness of the possibility of lowstand events can be an insightful guide in the search for new plays, and modern seismic surveys are a powerful tool for detecting and delineating key reservoir and sealing units.

Worldwide versus Provincial Petroleum Source Rocks

Arthur and Schlanger (1979) recognized the existence of “global anoxic events,” of Cretaceous age (Albian and Turonian), and speculated that they represented widespread organic-rich marine deposition. Klemme and Ulmishek (1991) identified six such events (which occurred in the middle Silurian, Late Devonian, Late Pennsylvanian-Early Permian, Late Jurassic, middle Cretaceous, and the Oligocene-Miocene), and showed that more than 90% of the world's oil and gas production and reserves originate from these six preferred stratigraphic intervals. The implications for exploration strategy are obvious— high priority should be given to those basins and plays containing marine sedimentary rocks of any of those six ages.

At the same time, the geoscientist must not ignore petroliferous source-facies that are more provincial or even local in origin and extent, such as foundered structural basins, euxinic back-reef depressions, and lacustrine deposits. However, such secondary source-rock units may often be understood only in context with sequence stratigraphy, basin tectonics, and geologic history.

The Kitchen—Geochemical Modeling

Demaison (1984) demonstrated that most of the world's petroleum occurs in “petroleum-generative depressions” and recorded four important characteristics of such generative basins:

Such trends and areas typically have high exploratory success ratios;

They are mappable by integrated geologic/ geophysical/geochemical methods;

Large petroleum accumulations tend to occur near the center of such petroleum-generative basins, or on adjacent structural high trends; and

Migration distances commonly range in tens rather than hundreds of miles and are limited by the drainage areas of individual structures.

Sluijk and Nederlof (1984) provide an excellent illustration of these basic ideas (Figure 31). Murris (1984) and Sluijk and Parker (1986) demonstrated that synthesis of these integrated geochemical/geologic methods (in combination with trap-size considerations) results in greatly improved exploration results (Figure 32). Later refinements of Lopatin's and Waples's ideas on time-temperature index as an indicator of petroleum generation, through realization of the strong kinetic influence on the process (Hunt et al., 1991; Waples, 1994) and sophisticated probabilistic computer modeling of the oil-generative process (example: “Basin-Mod” software and others), have now enabled geoscientists to make more reliable predictions about the time and relative quantity of petroleum generation, as well as the probable direction of migration out of the kitchen toward the basin margins.

Explorationists engaged in play analysis must understand thoroughly the genetic relationships between the family of related traps we call a play and the kitchen from which the traps were charged with oil and gas. Fortunately, the petroleum system concept facilitates that analysis and understanding.

Many diverse tasks are involved in conducting thorough, integrated risk analyses of exploration plays. Many geologic, geophysical, and geochemical facts must be gathered, interpreted, assimilated, and displayed, to be evaluated along with geostatistical, financial, and operational considerations. Moreover, the procedure should be carried out in consistent ways throughout the organization, for purposes of completeness and comparison with other plays being considered. Accordingly, many firms have developed play-analysis checklists to guide geoscientists in the investigation, analysis, and representation of such plays and trends. Appendix E is an example of such a checklist. When such a checklist is properly used, the result is that all available significant geot-echnical information has been gathered for use in the risk analysis of the play as a full-cycle business venture. Such a play risk analysis, however, usually is carried out as a separate flow sheet or software program.

It is also desirable for geoscientists to develop consistent, expressive symbologies and mapping techniques that contribute to such synthesis, represented as play maps (White, 1988), which convey to exploration decisionmakers the key geologic relationships and uncertainties that bear on a play's definition, prospectivity, uncertainties, and risks. Each company should adopt a consistent geologic style by which essential information may be synthesized objectively for management's consideration.

Use of Analogs in Play Analysis

Exploration play analysis requires geoscientists to make informed geologic and geostatistical predictions under conditions of great uncertainty. Frequently, direct evidence bearing on key geologic criteria is sim-ply not available. However, knowledgeable geoscien-tists may make surprisingly accurate indirect forecasts by using appropriate analogs.

An example of the effective use of analog FSDs, adjusted appropriately for geologic differences and exploratory maturity, is presented later in a west Texas case where the two subject areas were indeed geologically analogous.

In a broader context, explorationists should always be alert for documented analog examples of the basins, plays, and fields they are studying in the course of evaluating new plays. The international geologic literature is rich in classifications and documented studies of basin types, classes of plays and trends, types of different fields, FSDs, and other geostatistical data.

To maximize the effective use of analogs, three conditions must be met:

Geoscientists must have ready access to the international geologic literature. A qualified geotech-nical librarian, with keen organizational and computer-search skills, greatly facilitates such capabilities;

Statistical databases, organized to be geologically discriminating, allow geologic class and type to be related to pertinent geostatistics; and

Geoscientists must adopt a working philosophy that recognizes the likelihood of substantial uncertainty and embraces the use of analogs to help make estimates where direct evidence is absent.

Key Concepts and Techniques for Risk Analysis of Exploration Plays

Field-size Distributions

The topic of FSDs was introduced in Chapter 2, relative to prospect risk analysis, where the FSD was used as a reality check (p. 12). In play analysis, knowl-edgeable manipulation and utilization of FSDs, in combination with estimates of total fields to be found in the play, is an extremely powerful technique that allows surprisingly accurate forecasts of total play reserves, and prediction of approximate field sizes present in new plays.

Concepts and Principles

The FSD is the statistical expression of all fields found within a basin or play to date. It represents the effective natural synthesis of three factors:

The natural petroleum endowment of a play, including

the abundance and efficiency of hydrocarbon generation and migration, and

the number and capacity of traps

The relative maturity of exploration in the trend, including

numbers of exploratory wells and discoveries, and

optimization of current geotechnology in exploration, drilling, and development.

The relative efficiency with which industry can sense and discover larger, richer fields early and leave smaller, less profitable fields to be found in the later stages of the exploration cycle.

Discovery Process Modeling

Item 3 above touches directly on the concept of discovery process modeling, first expressed in 1958 by Arps and Roberts: the likelihood of discovering a field of a given size class is proportional to the geometric probability of its discovery and to the efficiency of exploration (Equation 8).

where

FA(w) =

cumulative number of discoveries estimated to be made in size class A by the drilling of w wells

FA(∞) =

ultimate number of fields in size class A that occur in the basin B = area of basin

A =

average areal extent of the fields in the given size class

w =

cumulative number of wildcat wells, and

C =

efficiency of exploration

It is important to understand that C is a dimension-less number estimated for each size class, and that C may vary, being higher for the larger size classes and declining gradually as size class diminishes unless new concepts or technology cause temporary increases in exploration efficiency.

When C = 1, discovery is random. For 1.5 to 3.0 MMBOE fields in the Denver Basin and Texas Gulf coast, C ≈ 2.0 (Drew, 1990). The commonly observed pattern is for C to increase (as size class of fields increases) ultimately to values approaching 6 or even greater for very large fields. Four very important conditions must be emphasized:

Sampling without replacement is assumed;

The area of the play remains constant;

Exploration technology is either constant or its improvement is regular; and

There is a consistent proportion between field ultimate recovery and field productive area— that is, fields with large reserves have large surface areas. Generally this is a valid assumption.

A good way to estimate Cs for each of the significant field-size classes of a future play would be to calculate C for analog field-size classes for several well-explored analogous plays or basins, and to apply it to the anticipated exploration campaign. Obviously, you must have some idea of the natural endowment of oil and gas accumulations that are undiscovered in the play (FSDs and estimated field numbers provide that) and apply analog C values to that data set. For perspective, here are some documented values for C:

C = 1 where discovery is random;

In mature petroleum plays such as Miocene-Pliocene of the onshore U.S. Gulf Coast, typical C values range from less than 2 for small fields (0.1 MMBOE → 10 MMBOE) to as much as 5 for very large fields (≈ 300 MMBOE).

For west Texas fields of 1.5 → 3.0 MMBOE, C was 2.0; similar values for C were calculated for Denver Basin fields.

If there have already been some discoveries in the play, you can usually use those data to project roughly what future discoveries will be, for field number and size—that is the basic premise of discovery process modeling. Keep the basic assumptions and conditions in mind, however!

Discovery process modeling can be useful in play risk analysis because it provides a method by which estimates of numbers of future discoveries can be independently confirmed or supported. In a partially explored play, FA(w) is the total number of fields dis-covered of size class A, and that value is easily derived just by counting the number of such field discoveries to date. FA (∞) is the projected ultimate number of fields of size class A that exist in the play—this value is the “unknown” in the equation. The difference between FA(w) and FA(∞) is the number of fields of size class A remaining to be found, theoretically. The critical parameter, C, cannot be determined—only esti-mated—which may constitute a serious drawback to the method. Moreover, results are extremely sensitive to the estimated area of the play.

Creaming Curves

During the course of exploration in a play, there is a clear tendency for the larger fields to be found in the earlier stages, whereas during subsequent stages steadily smaller fields are discovered. Geotechnically guided exploration is more efficient than random drilling, especially during the early stages of play exploration. Exploration inefficiencies result mostly from the fact that, while geoscientists generally can identify prospects too small to contain a large petroleum accumulation, they cannot reliably predict whether a prospect of large area or volume actually contains small reserves (because of leaky traps, poor reservoir quality, or falsely optimistic seismic data or interpretations).

This pattern has been recognized by authorities who present and analyze “creaming curves” (Forman and Hinde, 1986). By plotting cumulative reserves discovered by successive discoveries (or successive exploratory wells), relative exploration maturity of a play is indicated. When the discovery curve is rising steeply, explo-ration is efficient and profitable because large reserves are being found by relatively few wells. However, when the discovery curve flattens out, relatively small reserve additions are being added during later-stage drilling (Figure 33).

Creaming curves express the relationship between average exploration efficiency among all size classes and the natural endowment of oil and gas accumulations in the play or basin. The flattening slope of the creaming curve through time reflects decreasing average exploration efficiency among all size classes, acting on the natural endowment of oil accumulations of differing sizes, which as we know takes a natural log-normal form. Because more small accumulations are developed onshore than offshore, we expect creaming curves to be more pronounced in onshore trends.

An interesting example of efficiency variations in international exploration was presented by Shell (Murris, 1984) and previously introduced as Figure 32. Here, random exploration (C = 1) is represented by the straight, sloping line from lower left to upper right. Use of only the selective factor of estimated trap size generated forecasting efficiency that was 28% better than random. However, use of geochemical parameters plus trap size produced forecasting efficiency that was 63% better than random. Note that this expression of efficiency can be calculated at any stage of maturity, and it applies to discovery of all size classes, not individual size classes. Efficiency is expressed graphically as the percentage of all space between the diagonal random line and the optimum efficient case on the left. By analogy, exploration efficiency in the Gippsland Basin was clearly greater relative to trap volumes than to trap areas (Figure 33).

Shift of Field-size Distributions

As previously pointed out (p. 11 and Figure 5), when we separate a given play into earlier and later groups of field discoveries and plot them on a single log-probability graph, we can see the “daughter” FSDs shift steadily toward smaller field sizes during the life of the “parent” play. Figures 5a and 5b illustrate this tendency, where more than 100 fields in the Minnelusa Trend (northeastern Wyoming, U.S.A.) were separated into (1) the earliest one-third of all discovered fields; (2) the middle one-third; and (3) the latest one-third of all fields found.

Although there is considerable overlap in field sizes among the three daughter distributions, the overall reduction in field size is significant: mean field sizes drop from 4.58 MM (first third) to 2.17 MM (second third), to 0.76 (last third), for complete, untruncated distributions. For distributions truncated at the commercial limit (0.1 MMBOE), the respective differences in mean reserves are larger: 5.51 MM, 2.98 MM, and 1.06 MM.

Estimating Field Numbers

One of the essential geotechnical tasks required to carry out meaningful play analysis is to estimate how many petroleum accumulations remain to be found in the play area. Although the development of such estimates may be reviewed with skepticism by novice explo-rationists or non-geoscientists, useful projections can indeed be generated using several tested approaches outlined as follows.

Counting Visible Anomalies

If a seismic grid is available over part or all of the play area, estimates of the number of fields can be derived simply by counting all anomalies and reducing their number via estimates of the chance of geologic success (Pg) and of economic success (Pmefs). Where surface geology relates to numbers of prospects, land-sat images may indicate the density of anomalies.

Probabilistic Range of Field Number Estimates

Of course, we cannot know just how many petroleum accumulations are present, awaiting discovery, in a play fairway. But we can develop plausible predictions by using analog basins and trends, employing different inde-pendent reality checks, and expressing our estimates as probabilistic ranges. For example, we might be quite confident (P90%) that at least one field is present (given that an active petroleum system exists in the play area), with a best guess (P50%) of four fields, and be 10% confident in as many as 16 fields. Note that this distribution is probably lognormal.

Such a probabilistic expression can be combined with the projected FSD for the play to give a probabilistic distribution of play reserves, a key goal for play analysis.

Field Density in Analog Plays

Careful counts of the numbers of fields in one or more analog plays usually yields characteristic ranges of field numbers per unit area (i.e., 1.5 to 5.0 fields per 100 square miles [260 km2] or the reciprocal expression, 1 field per 20 to 67 square miles [52-174 km2]). Obviously, the desired value represents the ultimate number of fields, so the stage of exploration of the analog play must be taken into account. Note that such ranges lend themselves to probabilistic expression.

Field Area versus Field Reserves in Discovery Process Model

For analog plays and trends, graphs can be made showing the relationship between documented field areas and the associated projections of ultimate recoveries from those fields. Use those graphs to assign appropriate areas to the different field-size classes (Table 13). For different field-size classes, use the possible exploration efficiencies as outlined on page 68. The final step is to utilize the appropriate area for each field-size class in the discovery process model (Equation 8), which will yield the expected number of remaining fields in each size class. The resulting value should be viewed as an independent reality check of your other probabilistic estimates of field number that you derived by analog experience.

Pragmatic Approach for Field Number Estimates

A useful rule-of-thumb approach to estimating field numbers has been suggested by Dr. Jeff Brown (personal communication) based on the observation that in thoroughly explored play fairways, approximately 10% of the trend/area will be under closure. Given that the average wildcat success rate is approximately 25%, we can thus expect about 2.5% of the trend/area to consist of productive closures. Given common incomplete trap fill-up, averaging perhaps 40% of the closure areas, this suggests that about 1% of any productive play should be occupied within productive field outlines. Utilizing the graphs showing the relationship between field reserves and field area (p. 68), and the expectation of lognormality for the FSD, we can “back out” reasonable estimates for the field numbers and their associated sizes. This can often be done by trial-and-error methods as well.

Table 13

System of graduated size classes used currently by the U.S. Geological Survey (after Drew, 1990).

Observed Estimating Patterns

As pointed out previously, most geoscientists tend to underestimate the numbers of fields that will ultimately be found in a prospective play (given success) and overestimate the sizes of the those fields.

Use of Field-size Distributions in Play Analysis

It is not possible to carry out a geologic and economic play analysis without generating a projected FSD for the subject play, whether it represents an undrilled play in a frontier area or a later-stage entry into a proven play that is thought to still have economic potential.

The basic principles outlined earlier provide the basis by which explorationists can analyze and shift projected FSDs for those plays that are being considered for possible entry and participation by their companies. Manipulation of FSDs can be classified and discussed usefully in relation to play maturity.

Where Production Is Well Established

Companies may consider entering an existing play where they identify remaining profitable exploration potential, often anticipating changes in

increased profitability via reduced drilling costs, more effective stimulation/completion techniques, or more efficient production operations; or

new geologic concepts or perspectives about the trend.

In such areas, the existing play FSD should form the basis for the projected FSD for purposes of play analysis. Where new areas of the play appear prospective, the parent FSD itself can be employed. But where anticipated additional exploration will take place in the existing play area, the projected FSD should be compatible with the most recent 10 to 20 discoveries, depending on the timeframe involved. It is important that analog FSDs reflect the use of similar geotechnical techniques and concepts. That is, if new technologies demonstrate or promise a clear improvement in average discovery size, it is permissible for explorationists to shift the FSD toward slightly larger average field sizes. However, in most cases, projected FSDs in established basins should reflect recent discoveries, or shift toward slightly smaller average fields. In such cases, a reliable and complete catalog of producing fields provides the basis for constructing the projected FSD.

Where Limited Production Exists

In petroliferous trends where previous production consists of small fields because of poor reservoir performance, new tools (that allow areas of higher porosity and permeability to be detected) may generate discovery of larger fields for a limited time. Similarly, improved stimulation techniques and/or more optimal well locations may allow greater per-well recoveries, resulting in larger fields. Such expectations, if prudent and documented, should be reflected through manually shifted FSDs. New tools such as 3-D seismic and/or direct hydrocarbon indicators (DHIs) may allow explorers to sense traps that were previously invisible. Although the population of the remaining trend FSD will not change, it is likely that, for a limited period, dis-coveries will preferentially include the larger remaining fields, rather than the smaller ones, as previously discussed under Creaming Curves.

Another common class in this category applies to those trends in which only a few fields have been discovered but have not been developed to the point that reasonable predictions can be made for their approximate reserves sizes, or the key data bearing on reserves sizes are proprietary and not available. In such cases, geoscientists must use whatever data can be obtained to make range-type estimates of key parameters such as areas of closures (land-sat images, air photos, geologic maps, seismic data, etc.); average net pay thickness (outcrop studies, stratigraphic projections, well logs, reported perforation intervals, seismic data); and HC recoveries (sources reported above, plus analog fields, test reports, and extrapolations made therefrom). It may be possible to estimate a range of possible field numbers based on anomalies present on air photos, geologic maps, or subsurface seismic maps. It is often helpful to identify other producing areas that appear to be geologically analogous, utilizing FSDs, field numbers, and field densities from such counterpart areas, as well as more specific analogs bearing on prospect areas, reservoir thickness and character, and producing characteristics. The final product of such studies should be a catalog of projected field sizes that follow a lognor-mal distribution and a probabilistic distribution of possible field numbers.

Where Production Has Not Been Established

Three approaches can provide guidance here. The first uses ranges of geologic parameters from available outcrop or well data, air photos, seismic lines, and the like, as discussed previously. The second method relies on analog trends and plays in adjacent basins or geologically similar basins elsewhere, even on different continents. The key requirement here is that the subject basin and the analog basin must be geologically similar with respect to traps (structural style, reservoir lithology, and origin), sealing rocks, and petroleum endowment. The third approach may be applied in desperation—only when time or data limitations prevent the construction of a projected FSD for an unexplored, nonproductive new trend as described earlier. In this approach, the geoscientist constructs a generic FSD, represented at the low end (P99%) as a small, noneconomic or noncommercial field. Conceptually, this value might represent a small trap that was not correctly sensed by explorationists or a large trap containing only a thin attic-type oil accumulation resulting from a deficient HC-charge, absence of effective seal, fracture leakage, or extremely thin or poor-quality reservoir rock. Recommended values for such situations must be credible; they should generally lie

in the range of 0.01 MMBOE to perhaps 2 MMBOE,

depending on geometry and structural/stratigraphic style. At the upper end, the projected P1% value should represent the largest field that could credibly be expected in the trend, based on existing geologic knowledge. Generally this value will range from about

100 MMBOE to perhaps as much as 1 to 4 BBOE, if

such a large discovery is indeed remotely possible. Both the P1% and P99% values must be credible. A straight line drawn on a cumulative log-probability graph then represents the projected FSD for the subject trend. This method should be used only for new, nonproductive frontier trends where there is very limited information, and the projected values must be compatible with available geologic knowledge. Again, this method represents a last resort.

As a broad reality check the reader is referred to Figure 34, which shows the FSD of all oil and equivalent gas fields in the world. It is instructive to note the probabilistic distribution of world field sizes: P99% = 0.018 MMBOE, P90% = 0.25 MM, P50% = 5.4 MM, P10% = 143 MM, P1% = 1,917 MM, with the mean = ~ 100 MM. The writer is indebted to Richard Nehring and Petro-Consultants, Inc. for these data.

Manipulation of an analog FSD—by shifting it to reflect the expert's opinion that the subject play will be either more or less petroliferous—may reflect the following expectations:

More and larger fields, because traps are thought to be larger, reservoirs thicker and/or more perme-able, and top-seals more numerous and effective; or fewer and smaller fields, for the opposite reasons;

More and larger fields, because petroleum source rocks are thought to be thicker, richer, or more favorably situated with respect to migration pathways and/or timing, or fewer and smaller fields for the opposite reasons;

More and larger fields, because the subject play is thought to be at an immature stage of exploration compared with the analog play; or fewer and smaller fields, because the subject play is thought to be more mature than the analog play.

Prediction Using Analog Trends and Interpretive Shifts—An Example FSD

The Permian Basin of west Texas and eastern New Mexico is a world-class oil-producing province. In 1945, its northern extent (Area X) was poorly known, very lightly drilled, and essentially unproductive. Adjoining Area X on the south, however, was an area of similar stratigraphy and structural history and comparable size, Area Y, which contained 32 producing fields, nearly all with structural-stratigraphic traps in Permian-Pennsyl-vanian shelf-sandstone and carbonate reservoir rocks.

In order to construct a predicted FSD for Area X, the FSD for analog Area Y was first adopted as a proxy (Figure 35a). Then the proxy was assessed for plausibility. In this case the geoscientists concluded that Area X was probably not as petroliferous as Area Y because (1) one of two regional source-rock horizons was absent; (2) Area X was less deeply buried and cooler, and therefore less petroleum might have been generated, and then migrated and emplaced; (3) many of the Middle Permian porous carbonate formations of Area Y seemed to have been replaced in Area X by evaporites and tight dolomites through facies change; and (4) finally, the geoscientists did not believe Area X would prove to contain a field as large as the largest field in Area Y (Slaughter, 1,000 MMBOE).

Based on these geologic considerations, and working from the Area Y FSD, the geoscientists chose a pro-jected P1% field size of 400 MMBOE as plausibly the largest field that Area X was likely to contain. Recognizing that the low-side field size observed for the Area Y FSD (P99% = 1,500 BOE) probably would be similar for the Area X FSD, the geotechnical team then constructed a predicted FSD for Area X, using P99% = 1,500 BOE and P1% = 400 MMBOE (Figure 35b).

In 1945 the original Area Y FSD had an arithmetic mean field size of 45 MMBOE, with a median of about 1.5 MMBOE (see Table 14, column 1). Swanson's mean field size was 22 MMBOE. After downward adjustment for the aforementioned geologic reasons, the predicted FSD for Area X had a median field size of 0.8 MMBOE and Swanson's mean field size of 8.0 MMBOE (Table 14, column 2).

Compared with those 1945 predictions, what was the actual outcome in Area X (Table 14, column 3)? Fifteen fields were actually discovered in Area X during the next 12 years (1945-1957). The largest field was Anton-Irish (212 MMBOE); the smallest was West Petersburg (30,000 BOE). The actual FSD for these 15 fields is shown on Figure 35c, for comparison with the original Area Y proxy as well as the Area X prediction. All 15 fields are formed by structural and structural-stratigraphic traps in Permian-Pennsylvanian shelf sands and carbonates located along an irregular east-west regional transcurrent fault trend, the Matador Arch.

How well did this described predictive method actually perform? How do the predictions compare with the actual outcomes? Table 14 facilitates comparison by showing statistical parameters of the Area Y analog FSD (Column 1), the Area X Predicted FSD (Column 2), and the Actual Area X FSD (Column 3), as recorded 14 years later. In fact, the median field size was actually 1.3 MMBOE (vs. 0.8 MMBOE predicted), and Swanson's mean field size for Area X was actually 9.5 MMBOE (vs. 8.0 MMBOE predicted). The arithmetic mean field size for Area X proved to be 15.4 MMBOE. So the method generated reasonably accurate predictions, as documented by Table 14 and illustrated by Figure 35c.

Second-stage Example

Additional confirmation of the validity of this method for predicting FSDs in new areas is provided by the next stage of exploration in the region. In analog Area Y, 292 additional fields were discovered during 1945-1957. Arithmetic mean field size was 6.76 MMBOE, and the median field size was 0.34 MMBOE. Swanson's mean field size was 2.4 MMBOE (Table 14, Column 4). So, as Figure 36a demonstrates, fields discovered in Area Y (FSD 4 vs. FSD 1) were notably smaller in the 12 years after 1945 than previously. Comparison of Figure 36a with columns 1 and 4 of Table 14 quantifies this substantial reduction in field sizes: the Area Y FSD shifts to the left, toward smaller field sizes, by roughly one order of magnitude, in the second stage of exploration.

With such supportive data, the geotechnical professional staff of the exploration team began to have confidence that Area Y could indeed be used as an analog for Area X, given the appropriate adjustments for the geologic differences. To provide an independent test of this approach, we might reasonably expect that the shift in Area X FSDs from the 1945-1957 discoveries to the next cycle (1957-1995) would be comparable to the observed shift in analog Area Y FSDs between 1945 and 1957. In fact, Figure 36b confirms this expectation: the correlation is striking—the downward shift is of very similar magnitude, and slope changes are also quite comparable, providing compelling evidence for the applicability of this method.

During the period 1957-1995, 52 new fields were discovered in Area X. The arithmetic mean field size was 0.76 MMBOE and the median was 0.24 MMBOE. Swanson's mean field size was 1.58 MMBOE, based on the projected trend line of the FSD data points; based on actual field sizes, Swanson's mean field size was 0.82 MMBOE (Table 14, Column 5).

Four Additional Examples

Further evidence of the overall validity of such FSD manipulation is provided by the consistently downward shifts in FSDs of four plays in Area Y between 1945 and 1957 (Figure 37a, b, c, and d). Changes in the median values are substantial, averaging about 85% reduction between the two exploration stages. One partial departure should be noted: Leonard Dolomite FSDs show a marked steepening from the first to the second cycle, as expected. However, although there was a sharp decrease in the size of large fields found of this type, there was also an unexpected sharp increase in the size of small fields in this play. The result is that the mean field size actually increased slightly (0.18 MMBOE — 0.35 MMBOE) in the second exploratory cycle. This anomaly may be caused by the small sample size (n = 5) of the first cycle FSD; in any case, the economic significance was borne out that fewer large fields were found, as predicted.

a, b, c, d Downward shifts in FSDs in area Y between first cycle of exploration (pre-1945: a1, b1, c1, and d1) and second cycle (1945-1957: a2, b2, c2, and d2), for four plays.

Minimum Economic Field Size

The reader was introduced to the concept of minimum economic field size (MEFS) earlier, and its importance in play analysis has been discussed briefly. Early in the process of conducting a modern risk analysis of a proposed new play, it is necessary to estimate MEFS, which is defined as the threshold amount of producible oil and gas sufficient to recover—with interest—all exploratory capital investments required to establish the play as a profitable venture—or an economic failure. Ordinarily, the investments recovered include geologic and geophysical project costs (including seismic), lease or contract-area acquisition costs (such as bonuses, fees, etc.), costs of exploratory drilling and completion, and overhead, plus required interest. If one or more delineation or confirmation wells are required to warrant field development, those costs must also be included along with the anticipated costs to develop the discovered field. In other words, the discovered new field(s) must be at least large enough to generate, through production revenues, an after-tax NPV sufficient to pay for all investments required to establish the fact that profitable production does exist (PV > $0).

The use of $PV/BOE is a practical convention that includes development costs of successful discoveries because it is directly derived from DCF analysis of the discovered and developed field. Accordingly, where $PV/BOE is being used to establish MEFS, no additional provision should be made for development costs unless they are required to prove that an economic discovery has been made. In some cases, necessary pipeline costs may be included for product transportation. Reasonable and expectable initial production rates and percentage decline should be employed in the cash-flow analysis, which should also utilize the legal provisions, terms, and price structures of the anticipated contract. It is important for all estimates used in the cash-flow analysis to be rigorously and objectively evaluated, thereby eliminating bias of any kind—whether optimistic or conservative. Estimating ranges may be used where there is uncertainty about certain parameters, and probabilistic project NPVs may be calculated through Monte Carlo or Latin Hypercube simulation. Equation 9 provides an example calculation of MEFS for an onshore play in a mature trend, assuming two fields.

One of the most harmful conservative biases concerns the practice of requiring a single discovery to cover all exploration investments—that is, for MEFS to apply only to a single field. This usually results in the requirement that one large field be discovered. In fact, however, if a company is successful at all in a new play or large contract area, the usual outcome is the discovery of several new fields. Accordingly, it may be a more likely scenario to require three 10-million-barrel discoveries or two 15-million-barrel discoveries rather than one 30-million-barrel new field. This leads to the concept of minimum economic reserves required (MERR), to provide minimum profits sufficient to cover exploration investments. The practical result of such an enlightened criterion is to prevent more large-area projects from being rejected because of unrealistic, conservative biases.

Economic Truncation of Field-size Distributions

In mature onshore U.S. petroleum provinces, FSDs of plays or basins contain constituent “fields” ranging down to as small as a thousand barrels each, or even less. In fact, such fields really represent shows of oil or gas that were completed only for business reasons, or from geotechnical or financial imprudence, but nevertheless must be included among all production, which is regulated by the state.

The useful terms commercial and economic were defined and discussed on page 38, as they applied to prospect-reserves distributions. Procedures for truncating such distributions were also reviewed. The same procedures apply to FSDs. Appendix F provides additional detail.

Pmcfs and Pmefs

By estimating MCFS and/or MEFS and finding the location of those reserves values on the FSD, we can estimate the probability of a commercial field (or larger) or an economic field (or larger) in the play, given at least one discovery of sufficient reservoired hydrocarbons to at least sustain flow. Those probabilities are Pmcf and Pmef, respectively.

Figure 38a shows an FSD of all fields in part of the prolific northern Midland Basin, west Texas: fields in the distribution range from 10,000 BOE (P99%) to 1,000 MMBOE (P1%). Generally, a well that encounters an accumulation of at least 10,000 to 20,000 BOE will flow.

In that area, any well that flows will probably be completed for production, if a reasonable profit can be expected on the investment in stimulation, tubing, and production equipment. Of course, such a commercial well would not recover the investments in seismic, leasing, or drilling—they would be classed as sunk costs. Accordingly, the commercial threshold in such a province might be approximately 10,000-20,000 BOE; naturally, that threshold might vary depending on the details attending any particular well, especially flow rate. So, for the Northern Midland Basin, Pmcfs equals about 99% to about 96% (Figure 38a).

However, for an exploration venture to be economic it must recover all investments involved to bring it into production, plus a reasonable profit, taking into account also the time value of money. To estimate the proportion of fields in the northern Midland Basin that are economic, we must first estimate the approximate reserve size required to generate such a profit (about 500,000 BOE), 8 and then determine the proportion of the FSD having that reserves value, or larger (Pmefs = 75%). By excluding the noneconomic segment of the parent FSD, the remaining segment has been truncated economically, just as previously described for prospect-reserves distributions (pp. 39-41). The smaller, noneconomic fields have been excluded, therefore the mean of the remaining distribution of economic fields will be larger than the mean of the parent FSD, just as previously described.

Offshore FSDs Are Already Truncated

Now, suppose the Midland Basin actually was located offshore, in waters up to 600 ft (~200 m) deep. The parent endowment of accumulations would be no different, but the actual FSD would be greatly attenuated. Because of the expense of offshore production facilities, FSDs of offshore plays reflect de facto truncation (Figure 38b). Ordinarily such truncation is commercial rather than economic. However, because the cost of offshore production facilities is generally very large in relation to exploration costs, the commercial threshold usually approaches the economic threshold for offshore plays. The same principle holds true for all plays in which very substantial financial commitments are required to bring a new field into effective production—for example, remote, ultra-deep, hazardous, or hostile provinces. It is important to emphasize this point because many explorationists seem to overlook it. Reconstruction of the parent FSD is possible, however, by reviewing all pertinent well data and logs in the play area, and by distinguishing dry holes that were uncompleted show holes from dry holes that did not encounter flowing hydrocarbons in the reservoir objective. Appendix F provides more detail on the particular procedures involved in such reconstructions.

Chance of Play Success

Geologic Chance Factors

A system of five discrete geologic chance factors was presented in Chapter 3, pertaining to prospect chance of geologic success (Pg), including definitions and descriptions of subcomponents involved in each of the five main categories (p. 34-36). The importance of the chance factors applying to plays as well as prospects was emphasized. Table 15 summarizes this recommended system.

Coincidence of Geologic Chance Factors

In order for the geologic chance factors to work, their influence must be operative in a common area— that is, their effects must coincide in time and space (pp. 34 and 48). Figure 39 depicts a hypothetical basin to illustrate the problem. Petroleum source rocks are present only in the eastern end of the basin bottom. Because buoyant migrating hydrocarbons move updip, at right angles to structural contours, it would be difficult (even impossible) to charge the faulted closures on the southwestern flank of the basin. Moreover, primary reservoir sands do not extend far enough southwest (updip) to be present in the area of the faulted closures. Also, the regional evaporate seal is not present in the western half of the basin. The result is a greatly reduced chance of success because the key geologic elements controlling the occurrence of reservoired petroleum accumulations do not coincide anywhere in this hypothetical basin.

Coincidence is one of the most significant elements that explorationists must assess, especially for new plays in frontier basins. It is probably one of the most commonly overlooked problems, which leads to very low industry success rates for high-risk new plays. Play maps, as described previously, provide an excellent way to address the coincidence problem.

Shared versus Independent Chance Factors

Earlier work by Baker et al. (1986), Baker (1988), and White (1993) discussed independent and shared chance factors, and the concept was reviewed on pages 41 and 62.

Consider the five elements of geologic chance— some will apply to all prospects in the play area, whereas others will vary among all prospects in the play area. Examples of frequently shared chance factors include the presence of mature hydrocarbon source rocks, hydrocarbon migration into the play area, and timing. Independent chance factors commonly include reservoir rock, closure, and containment. In some cases, subsidiary elements of each main chance factor may vary—for example, seal effectiveness (such as fault leakage) may be independent, causing some prospects in the play to be dry, but not all, whereas the regional preservation history (absence of biologic or thermal degradation) may be common among all prospects and therefore shared.

Table 15

Geologic chance factors required for play and prospect success.

Confidence (%) that thermally mature HC-source rocks are present in adequate volume, richness, and type to provide an HC-charge to the play area. Components:

quantity [thickness, extent, richness]

HC-type [oil, mixed, gas]

thermal maturity

Confidence (%) that hydrocarbons have migrated, utilizing conduits and carrier beds, along migration pathways into the location of existing closures in volumes adequate to charge them. Components:

conduits [carrier beds or zones]

migration routes

efficiency [concentration and transmission versus dispersion]

timing

Confidence (%) that reservoir rock is present in adequate volume, porosity, and deliverability to support one or more flowing wells in the play area. Components:

storage capacity [thickness, extent]

porosity

reservoir performance [permeability, drive mechanism]

Confidence (%) that structural and/or stratigraphic closures of adequate area and vertical relief are present in the play area and can be detected. Components:

adequate closures exist

confidence in mapping

Confidence (%) that effective sealing rocks are present and that emplaced hydrocarbons have been preserved (= containment). Components:

Effects of operative geologic chance factors must coincide in the prospective area.

This leads, then, to the separation of geologic chance factors for purposes of play analysis. Those elements are shared that affect all prospects in the play area. If a valid test establishes clearly that a shared chance factor does not exist in the play area, it condemns all prospects in the play area—or it forces significant revision of the play area outline (area of possible coincidence).

Finally, it is common to recognize partial dependencies—elements of geologic chance that are partially shared (= dependent) and partially local (independent).

Chance of Play Success Is Economic, Not Commercial

As previously discussed, the criterion for success of the exploration play is that the play must be economic, rather than commercial. The reason, of course, is that no responsible organization is going to continue to drill for fields that are merely commercial—after several such marginal discoveries in a play, management will perceive the folly of continuing to explore in a play that cannot be economic on a full-cycle basis. That is, they will not continue to drill for fields that do not generate production revenues sufficient to exceed the invested capital plus interest. Accordingly, such a play will be abandoned even though some marginal fields have been found.

A further complication is presented by the play which contains at least one economic field, but additional dry holes or seismic costs incurred in subsequent exploration for other counterpart fields add sufficient expense to transform the play from economic to commercial on a full-cycle basis. The solution to this problem is to identify, for various reserves cases, the maximum costs for additional drilling and seismic that can be incurred before the entire project, on a full-cycle basis, deteriorates from economic to commercial. After discovery of the first economic field, each subsequent discovery should bear the costs of the number of dry holes that would be consistent with the independent chance of economic success; i.e., if Pe (independent) is .25, each anticipated discovery should carry the cost of three dry holes, in addition to the cost of the discovery well.

Integration: Calculating the Chance of Economic Play Success

The purpose of this section is to explain and illustrate one of the mathematical procedures involved— utilizing various geologic and economic estimates —to calculate the estimated chance of economic play success (Equation 10).

Part 1 of Equation 10 shows the basic form of the equation, with the shared chance factors separated from the independent chance factors. Given that the play exists (shared chance = 1.0), the steps shown for the independent chance factors allow successive determination of:

average local chance of a prospect's geologic success (one test),

average local chance of a prospect's economic success (one test),

average local chance of a prospect's economic failure (one test),

chance of all economic failures in n local trials, and

chance of at least one economic discovery in n local trials.

This equation expresses the generally recognized truth that, if the play really does exist (i.e., the product of the shared factors = 1.0), then the chance of at least one discovery increases as the number of wells testing the independent chance factors also increases.

Part 2 of Equation 10 lists the estimates and assumptions required to carry out the calculation. For most established plays—even “undercharged” trends— there is usually more than enough hydrocarbon source rock and generated/migrated oil to supply the traps in the play, i.e., the adequacy of source rock and generated/migrated oil is usually not a critical problem. However, in frontier basins and plays, especially those requiring very large discoveries in order to be economic, the play analyst may be justified in assigning a probability to the likelihood that source rock and migration processes have been adequate to supply at least one economic field.

Part 3 of Equation 10 tracks the actual calculation using the equation from Part 1 and the values provided in Part 2. Play (= shared) chance is estimated to be 0.54: the analyst is quite confident of source rock presence (0.9), and moderately confident about generation/ migration adequacy (0.6). Given that the play does exist, the average chance of prospect success (flowing hydrocarbons) is 0.28, and the average chance of economic prospect success is 0.112. Therefore the average chance of economic prospect failure is the complement, 0.888. The chance of drilling four consecutive economic failures from such prospects is 0.622. Therefore the chance of making at least one economic discovery among four such prospects is again the complement, 0.378. Finally, the product of the shared and independent economic chance factors is 0.204, which represents the chance of making at least one economic discovery before quitting the play.

Note that these estimates of geologic and economic chance should be expected to change with successive wells, depending on what is learned from each test, as Baker (1988) explains. This is a very important point.

Summary of Geotechnical Data Required for Play Risk Analysis

After completing the studies required to carry out the geotechnical assessment of the new play (see Appendix E) and the resulting assimilated maps and compilations of pertinent data, our next step is to conduct the actual play analysis, the principal product of which is the evaluation of the play as a full-cycle economic venture. As previously described (p. 62), such analyses are usually carried out in a systematic way, using either a flow sheet or interactive computer software that is based on such a flow sheet.

In any case, 15 key geotechnical values are required to carry out the analysis (Table 16). Eight of these parameters are primary estimates made on the basis of geotechnical studies, or analog data. The seven remaining parameters are secondary values derived from arithmetic manipulation of the primary estimates. Risk analysis of the exploration play cannot proceed without these parameters, which in all cases should be compared with various reality checks for credibility.

Primary Parameters

Play (= Shared) Chance

Play chance is a confidence statement—the probability that in the area of the play, those geologic chance factors that apply to all prospects in the play are actually satisfied. Play chance is the product of the probabilities of the shared geologic chance factors that must be satisfied if there is to be at least one field large enough to sustain flow. It is not tied to economics or commerciality. A useful alternative definition might be the explorationist's confidence that an active petroleum system exists somewhere in the area and within the stratigraphic interval of the play. Theoretically, if a valid test establishes that one of the shared chance factors is not operative, it kills the entire play—or it requires significant revision of the prospective play area (p. 82). Characteristic elements of play chance often include (but are not limited to) generation, migration, timing, and preservation (Table 17).

Average Prospect (= Local) Chance

Another confidence statement, prospect chance is the likelihood (expressed as a probability) that those geologic chance factors that are independent (that is, that may vary locally, among all the prospects in the play) are, on average, satisfied in the area of the play. If the play really does exist (play chance = 1), then prospect chance is the average geologic success ratio of all prospects in the play area. It is the average product of the independent geologic chance factors. Common independent chance factors include reservoir, closure, and some elements of containment (Table 17). Remember that prospect chance equates only with discovery of a reservoired accumulation capable of sustained flow. It has no economic or even commercial implications— those constraints are derived values, considered later in the analysis.

Cost of Exploration

The cost of exploration is the sum of estimated prudent expenditures for seismic surveys (including processing), other geophysical data, other necessary maps, surface data acquisition, geologic and geochemical analyses, leasing costs (including bonuses and fees), exploratory drilling and testing, and overhead. It is the total amount that must be recovered (at interest) from future production revenues if the venture is to be at least minimally profitable (= PV > $0). This is the amount of money you are going to have to spend to know whether the project is a success or failure. In some conservative project evaluations, this amount is projected to be recovered through production revenues from only one discovery; in more reasonable evaluations, it may be recovered from several discovered fields (see p. 78). Conceptually, it also can be considered as the cost of a failed exploration play—and as such it should be tied to your minimum exploration program.

Projected Field-size Distribution

The construction of an estimated FSD for the subject play has already been described at length, whereby informed manipulations (= shifts) consider geologic attributes and exploratory maturity and efficiency. Regardless of the methods employed, the projected FSD represents a responsible estimate of the fields that will be found, given the discovery of one or more fields, and it is a key parameter for risk analysis of any exploration play. It is expressed as a lognormal cumulative probability distribution.

Projected Numbers of Fields

Another required primary estimate is the number of fields that will ultimately be discovered in the play area (p. 69). Note that such fields need not be economic. Ordinarily this is expressed as a lognormal cumulative probability distribution. The projected field number is usually determined by comparison with analogous areas, taking into account relative maturity of exploration, but it may also be estimated using the principles of discovery process modeling or other methods. Obviously this estimate assumes at least some future success in the play.

Number of Exploratory Tests

Given that the shared chance factors turn out to be satisfied in the play area (play chance = 1), then the probability of success is a function of the independent or local chance and the number of consecutive unsuccessful trials (dry holes) the firm is willing to drill before giving up. Usually, this number is partly political, based on management's preferences, but it is also a function of what specifically is learned from each trial. This involves considerations of Bayesian mathe-matics 9 (see Baker, 1988). In general, companies experiencing two consecutive tests that indicate deficiency in the shared chance factors are probably justified in abandoning the play. Consecutive dry holes that confirm the play chance factors, but appear deficient regarding the independent chance factors, might be allowed to number two to four or five, depending on what is learned from each dry hole and the anticipated capacity for geotechnical learning, adjustment, and improvement.

$PV per BOE of Discovered Reserves

The play analyst needs to be able to translate projected prospect reserves into present value in order to be able to estimate the present value of the entire play as a full-cycle economic venture. In some plays, a useful approximation of this parameter can be made from the median or mean field-reserves case and applied to fields of all sizes in the play. In most plays, however, the analyst is advised to prepare economic analyses (cashflow models) of key field sizes (P10%, P50%, P90%, Pmean), using the contract terms and tax schemes expected in the area. This is because in most plays, especially those with production-sharing contracts or offshore areas requiring expensive production platforms, $PV (in dollars) per BOE is not a constant. Variables such as initial production, decline rate, and wellhead price can be considered via such reserves cases. The ultimate product required is a simple graph showing field $PV as a function of field reserves and/or $PV per BOE for different field sizes.

Minimum Economic Field Size (MEFS)

MEFS has already been discussed in detail, and Equation 9 shows the method of calculation, which utilizes (1) cost of exploration; (2) $PV per BOE; and (3) minimum number of required discoveries.

Secondary (Derived) Parameters

Probability of Minimum Economic Field Size (Pmefs)

Pmefs refers to the probability, given a discovery in the play, that the discovered field will be equal to or larger than the MEFS. If several equal-size fields are anticipated to reach break-even economics, Pmefs is the probability of either of them (but not both). This parameter is derived using MEFS in conjunction with the projected FSD for the play, as described on page 79.

Projected Numbers of Economic Fields

This parameter is expressed as a cumulative probability distribution, ordinarily lognormal, derived from the projected number of fields distribution (p. 69), reduced by multiplying by Pmefs.

Chance of Economic Play Success

This parameter has already been discussed in detail (p. 82 and Equation 10). It is derived using (1) play chance; (2) average prospect chance of success; (3) Pmefs; and (4) number of exploratory tests. Note that this requires Pmefs to be combined with the other independent geologic chance factors.

Projected Economic Field-size Distribution

This FSD is derived from the original projected FSD for the play, economically truncated at Pmefs and redistributed through 98%. It is necessary to determine the new P10%, P50%, P90%, and Pmean of the truncated distribution as described previously and in Appendix F.

Projected Distribution of Economic Play Reserves

This distribution is the product of two distributions: (1) projected numbers of economic fields, and (2) projected economic field sizes. These two distributions are combined by Monte Carlo or Latin Hypercube simulation or by employing the analytical/graphical method described in Appendix B. The result is a cumulative probability distribution of economic play reserves. The mean of that distribution (mean economic play reserves) is statistically the best representation of economic play reserves.

Present Value of Economic Play Reserves

This distribution is the product of two distributions: (1) $PV/BOE, and (2) distribution of economic play reserves. The mean of the distribution is statistically the best expression of economic play present value.

Play Expected Present Value

The expected value concept was discussed previously. It is the chance-weighted present value of a pro-ject—it represents the play as an entire, full-cycle economic venture. It is one of the important economic measures by which competing plays can be usefully compared. It may be derived using (1) chance of economic success; (2) mean $PV of economic play reserves; and (3) cost of exploration (= cost of a failed play). As explained earlier, its weakness is that it assumes the firm is risk-neutral.

Process for Systematic Risk Analysis of Exploration Plays

Now we will set out the various steps and calculations required to carry out a geologically responsible, statistically sound assessment of the economic value of a given play, as previously discussed (p. 84). Note again that the values used in the flow sheet must be based on a sound and thorough geotechnical review of the subject plays, as outlined in Appendix E.

Many exploration firms utilize comprehensive computer software, or a standardized flow sheet, to carry out systematic play risk analysis. Either approach can bring companywide consistency to the process, which then leads to play inventories that, in turn, yield legitimate optimized values to those plays selected for execution—the play portfolio. However, geotechnical staff must be trained to understand the principles of play analysis if they are to use software (or flow sheets) responsibly.

Recommended Procedure

The following discussion, which also draws on prior work by Baker et al. (1986) and White (1992, 1993), is intended to guide the explorationist/analyst through the proper steps of economic play analysis.

Model and Map the Play as if It Already Exists

Depict all the requisite geologic elements in the contemplated area, as if they already exist—the following analysis is then designed to estimate the likelihood that your picture is essentially correct. In particular, you must show the areal and spatial distribution of geologic features, on maps and sections, in relation to the play fairway and to locations of specific prospects—if you have that much data. Remember what you are risking geographically: the chance that at least one flowable petroleum accumulation (having the geologic attributes described) really does exist in the play area, in the stratigraphic succession identified.

Estimate the Play (=Shared) Chance

Based on Table 17, circle those geologic chance factors that are shared, i.e., common to all prospects in the play, and enter your confidence (probability = decimal fraction) that each factor will indeed prove to operate or exist in the play area. If you are essentially certain that a shared factor exists throughout the trend area, assign it a probability of 1.0. Then multiply the circled chance factors—the product is play chance or shared chance. NOTE: Individual chance factors may be partly shared, and partly independent, indicating partial geologic dependency.

Estimate the Average Prospect Chance

Under Item 1 of the flow sheet (Table 17), circle those geologic chance factors that are independent, that is, that vary among the prospects in the play area. Assign to each your confidence (= probability) that the chance factors will be satisfied for average prospects, and multiply them. Some chance factors may be partially dependent, so you may enter decimal fractions in both the shared and the local columns. If you have sufficient data, determine from several prospects in the play area your average confidence (= probability) in the existence of each of the independent chance factors and multiply them. 10 Their product is average prospect chance (prospect Pg). If the play proves to exist (i.e., play chance is 1.0), then the average prospect chance will be the overall success rate of prospects in the play. Some reality checks are:

If the play is a future projection of an ongoing play, is the historical exploration success ratio compatible with your estimate?

If the play is undrilled but you have a comparable productive analog, what is the average prospect success rate of the analog play? Be sure you take modern technological capabilities into account.

Use White's “prospect grading scheme” if you have enough data to identify the location of many of the prospects in the play, as shown in Equation 11. The problem with this approach is that commonly, if you have sufficient data to be able to identify and grade so many prospects, you're already in the play! That is, you have already invested capital for geotechnical information— the decision to enter the play has already been made!

If you have absolutely no idea about the average prospect success rate, remember that the world average for the petroleum exploration industry has been consistently about 25% for the past 40 years (Pcommercial, not Pgeologic!).

Reality check: If you have an inventory of prospects in the play, be sure the average Pg of such identified prospects is consistent with average Pg for the play.

How Many Consecutive Exploratory Dry Holes before your Firm Quits?

Based on your firm's risk propensity and past history and assuming your wells establish that the play chance factors appear to be satisfied (play chance = 1.0), estimate the number of consecutive dry holes your management would be likely to drill before abandoning the project. Reality check: Following Baker (1988), this number ordinarily should be more than one and usually no more than four or five. Key question: What do you expect to learn from each dry hole? Important note: This assumes only one play concept to test.

Estimate the Cost of Geotechnical Exploration

What is the area of the play? How much will regional gravity and magnetics cost? Interpreted land-sat imagery or air photos? Surface geologic mapping and/or sampling? How much seismic data (regional 2D, local 2-D, and 3-D data) will be required? At what cost? How many successive exploratory dry holes would your company drill in the play before declaring the venture a failure? What will they cost? Remember to include nonrecoverable bonus and lease costs here, as well as overhead, and make all estimates on an after-tax basis. Reality check: Another term for this value is the cost of exploration failure.

Estimate the Number of Fields in the Play

Based on the size of the play area, structural and stratigraphic grain or complexity, and field density in analog trends, forecast the total number of fields that will be discovered in the play area, given that there is at least one discovery. This forecast should take the form of a probabilistic range with a lognormal distribution (see p. 69). An alternative method is to employ discovery process modeling principles to derive these estimates (see p. 69). Remember the common biases: Most explorationists underestimate the number of future fields in plays and overestimate the reserves sizes of those fields, especially in onshore areas.

Analyze the Terms of the E&P Contract

Be sure to incorporate all relevant terms, including taxes, into cash-flow models that you develop for possible project scenarios. Identify particularly critical, sensitive, and useful provisions that should influence your negotiations and/or business decisions.

Construct a Field-size Distribution for the Play

Details have been discussed (pp. 67-76 and Appendix F). Subject the projected FSD to reality checks.

Find Net Present Value for Different Field Sizes

Run cash-flow models on P10%, P50%, P90%, and Pmean reserves cases under the operative contract terms and using the current company discount rate. Construct a graph showing the relationship of NPV to recoverable reserves (p. 86). Also run cash-flow models for the total failure case and at least one partial failure case to determine the negative NPV for those outcomes. All values should be estimated on an after-tax basis.

Estimate Minimum Economic Field Size (MEFS)

Use the graphs and data from the previous item to estimate the minimum economic reserves required (MERR) to recover the cost of exploration at interest. Then, given at least one economic discovery, what is a reasonable low-side outcome—one field discovery? Two? Three? Whichever is the most reasonable outcome will determine MEFS. Reality check: Large play areas and geologic complexity both encourage field numbers larger than one, but prudence suggests that this field number should not exceed three. Short exploration time periods (contractual), limited exploration budgets, and risk-averse management all drive the estimated field number downward toward one.

Find the Probability of a Field of Minimum Economic Size (Pmefs)

On the FSD, find the probability associated with a field of minimum economic size. Pmefs is the proportion (%) of the FSD that is of MEFS or larger (see pp. 79 and 86).

Find NPV/BOE for Different Field Sizes

Using the data and graphs from net present value, construct a graph and/or table that shows the NPV per oil-equivalent barrel to your firm, as operator (p. 86). This will allow your prospectors to readily assign an approximate value to every prospect, based on mean reserves, given discovery (and also to calculate expected value, by incorporating average prospect chance and assuming play chance = 1.0). All values reflect an after-tax basis.

Estimate the Number of Economic Fields in the Play

This estimate is derived from the distribution of number of fields in the play by multiplying each of the P10%, P50%, P90%, and Pmean field numbers by Pmefs to generate a new probability distribution for numbers of economic fields, given at least one discovery. Since these estimates represent statistical values, it is permissible for these values to be decimal fractions.

Find the Chance of Economic Play Success

This calculation has already been discussed and illustrated (p. 82 and Equation 10). It is derived using the following:

play chance = product of shared factors

average prospect chance = product of local or independent factors

number of consecutive dry holes before failure

probability of MEFS (Pmefs)

Construct the Economic Field-size Distribution

This distribution of economic field sizes is derived by truncating the original FSD constructed for the play (p. 87). It is essential to determine the new P10%, P50%, P90%, and the Pmean reserves values for the economic part of the FSD, as described on page 79, and in Appendix F.

Find the Mean Economic Play Reserves

The next step is to construct a cumulative probability distribution of economic play reserves. This is accomplished by combining the distribution of economic field sizes and the estimate of the number of economic fields in the play, ordinarily through conventional Monte Carlo simulation or the Latin Hypercube method (p. 94). An analytical method to combine either two or three such distributions, using graphical procedures, has been developed and described by Capen (1992), Megill (1992), and Rose and Thompson (1991), as discussed in Appendix B. The mean of the economic play reserves distribution is the best statistical expression of play reserves potential, but the whole range of possible outcomes can be calculated.

Find the Mean NPV of Play

This range of values, which assumes economic play success, is derived on the basis of the following:

mean play economic reserves

NPV/BOE for different field sizes

This may be generated as a probability distribution, with P90%, P50%, and P10% values determined. However, the mean value is the best statistical expression of play NPV and should be calculated on an after-tax basis.11

Calculate the Play Expected Value (ENPV)

This value is the chance-weighted statistical value of the play. Play ENPV utilizes the following parameters:

chance of economic play success and chance of failure

the mean NPV of the play (including additional exploration costs associated with P50 and P10 field numbers)

play cost of failure

It should be calculated on an after-tax basis.

Determine the Lease/Acquisition Price

This determination will vary widely, depending on the method by which contract rights are to be acquired— sealed bid, oral auction, performance bid (= work commitment), serial negotiations, or private treaty. This topic is discussed in more detail in Chapter 6, in the section on Acquisition Strategies (pp. 93-99). There are several cautionary notes:

If there is a reasonable likelihood that the host government or landowner will allow subsequent renegotiations of contract terms, the fundamental strategy of competitive sealed bidding, based on bidding a fraction of project expected value, is negated (see p. 99). In such situations, a winning strategy may very well involve tactics of deliberate overbidding to obtain the license, followed by later negotiations. This is a dangerous game, however, and disavows the sanctity of contracts.

The widely described (Capen, Clapp, and Campbell, 1971; Thaler, 1992) systematic reduction of ENPV (.35 χ ENPV) utilized by ARCO and others in the Gulf of Mexico (to guard against the Winner's Curse) is compatible with chances, reserves variances, and values of that petroleum region. But in many new play areas, chances are lower, and reserves variances even higher, justifying a more severe reduction, even lower than 35%, to perhaps 25% or even 20% of ENPV. On the otherhand, in acquisitions having higher anticipated chances of success and smaller reserves variance, the 35% reduction should be relaxed to 50% or perhaps even 60% or 70% for producing properties.

It is also extremely important to consider the overall distribution of bid amounts by competitor companies in previous offerings and sales, in the area of interest, in analog areas, and in other recent sales. Often, especially in marginal or high-risk plays, the actual prevailing bid levels expressed as dollars per acre (or any other area measure) may be much less than your 35% or 25% reduced bid determination!

In estimating cost of geotechnical exploration (= cost of geotechnical failure), it may be necessary to perform several iterations to develop an appropriate value representing the land portion of costs (= winning bid). Such iterations may impact values of MEFS, Pmefs, etc.

Calculate the Key Economic Measures for the Play

To provide a basis for evaluating the play as a business venture, or to compare it with other opportuni-ties, it is useful to calculate the anticipated values for key economic measures, such as:

discounted cash flow rate of return (DCFROR) (calculate at P10%, P50%, P90%, and Pmean economic reserves level). DCFROR is only useful as a minimum qualifier, or hurdle—it is not useful to compare projects;

investment efficiency (calculate on a risk-adjusted basis; see p. 54);

cost of finding (calculate on a risk-adjusted basis, i.e., include the costs of dry holes).

By subjectively ranking the various acreage blocks within the play area, it is then possible to allocate play reserves, ENPV, exploration costs, and bids among the prospective blocks. This will be useful in carrying out various negotiations and business operations as the play is carried out.

8Naturally, we must recognize that 500,000 BOE should not be considered as a precise value. The economic threshold will vary according to the influence of depth, completion costs, flow rates, transportation, wellhead price, and operating costs, including taxes. Nevertheless, it is possible to generate an approximate minimum economic field size for fields in a play because plays are generally constrained by similarities in the above factors.

9Following the concepts and formulae of Thomas Bayes, an 18thcentury English mathematician, “the primary application of [Bayesian mathematics] is to use new information—‘learning’—to revise probabilities based on old information, that is, to compare posterior probability with the priors” (after Bernstein, 1996). In other words, we revise inferences about old information as new information arrives. Dealing with conditional probabilities requires employment of Bayesian principles.

10However, see page 46 for additional perspective on this scenario.

11For the anticipated P50 or P10 numbers of fields, we must also account for the costs of exploratory drilling (especially the unavoidable dry holes) and additional leasing costs associated with such a successful play. Therefore, the mean play NPV must include such costs to be correctly represented.

Depositional topography interpretation indicates that all of the shelf limestone is older than any of the marine shale. The only facies relationship is between the shelf limestone and marine limestone, with basin starvation inferred.

Depositional topography interpretation indicates that all of the shelf limestone is older than any of the marine shale. The only facies relationship is between the shelf limestone and marine limestone, with basin starvation inferred.

During the 1990s, many international petroleum companies improved their exploration performance significantly by using principles of risk analysis and portfolio management, in combination with new geotechnologies. While exploration risk cannot be eliminated, it can certainly be reduced substantially, on a portfolio scale. And the widespread adoption of standardized risk analysis methods during the 1990s brought badly needed discipline to petroleum exploration. By the mid-1980s, most well-informed major international petroleum firms that were engaged in exploration recognized that, globally, the average size of new discoveries was diminishing. Not coincidentally, the class of exploratory prospects categorized as “high risk/high-potential” was showing marked signs of underperformance. For major companies, when all such ventures, which averaged around a 10% perceived probability of success, were considered, less than 1% actually discovered profitable oil and gas reserves, and the sizes of these discoveries were generally far smaller than predicted. All in all, such exploration for new giant fields destroyed value, rather than creating it, in the 1980s and early 1990s. Consequently, exploration, as a corporate function, lost credibility. It badly needed to begin delivering on its corporate promises. It needed to become more efficient, and thereby more profitable. To optimize the allocation of exploration capital, concepts of portfolio management began to be considered.